Abstract: Systems and methods for realigning streams of neuron outputs are provided. An example method may include generating, by a processing unit, neuron outputs including at least a first neuron output and a second neuron output, generating, by at least one further processing unit, further neuron outputs including at least a further first neuron output and a further second neuron output, receiving, by a synchronization module communicatively coupled to the processing unit and the further processing unit, the neuron outputs, wherein the neuron outputs and the further neuron outputs are received in an arbitrary order, and ordering, by the synchronization module, the first neuron output, the further first neuron output, the second neuron output and the further second neuron output according to a further order, the further order being different from the arbitrary order.

Abstract: Systems and methods for accelerating artificial neural network computation are disclosed. An example may comprise selecting, by a controller communicatively coupled to a selector and an arithmetic unit and based on a criterion, an input value from the stream of input values of a neuron, configuring, by the controller, the selector to provide, dynamically, the selected input value to the arithmetic unit, providing, by the controller to the arithmetic unit, an information of the selected input value, acquiring, by the arithmetic unit and based on the information, a weight from a set of weights, and performing, by the arithmetic unit a mathematical operation on the selected input value and the weight to obtain a result, wherein the result is to be used to compute an output of the neuron. The criterion may include a comparison between the input value and a reference value. The reference value may include zero.

Abstract: Systems and methods for realigning streams of neuron outputs are provided. An example method may include generating, by a processing unit, neuron outputs including at least a first neuron output and a second neuron output, generating, by at least one further processing unit, further neuron outputs including at least a further first neuron output and a further second neuron output, receiving, by a synchronization module communicatively coupled to the processing unit and the further processing unit, the neuron outputs, wherein the neuron outputs and the further neuron outputs are received in an arbitrary order, and ordering, by the synchronization module, the first neuron output, the further first neuron output, the second neuron output and the further second neuron output according to a further order, the further order being different from the arbitrary order.

Abstract: Systems and methods for accelerating artificial neural network computation are disclosed. An example may comprise selecting, by a controller communicatively coupled to a selector and an arithmetic unit and based on a criterion, an input value from the stream of input values of a neuron, configuring, by the controller, the selector to provide, dynamically, the selected input value to the arithmetic unit, providing, by the controller to the arithmetic unit, an information of the selected input value, acquiring, by the arithmetic unit and based on the information, a weight from a set of weights, and performing, by the arithmetic unit a mathematical operation on the selected input value and the weight to obtain a result, wherein the result is to be used to compute an output of the neuron. The criterion may include a comparison between the input value and a reference value. The reference value may include zero.